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\"Freezing people in time turns them into shadows,\" the caretaker continues, \"and no known technology can bring them back. Because iftime stops, even light can't escape, and there's no way to change anything.\" According to my records, she went into stasis because she had cancer.

The soil freezing characteristic curve (SFCC) plays a crucial role in investigating the soil freezing‐thawing process. Due to the challenges associated with measuring the SFCC, there is a shortage of high‐quality or rigorous test results with sufficient metadata to be effectively used for applications. Current researchers typically conduct freezing tests to measure the SFCC and assume a singular SFCC when studying the freezing‐thawing process of soils, although limited studies indicated that there is a hysteresis during the freezing and thawing process. In this paper, a series of freezing‐thawing tests were performed to assess the SFCC, utilizing a precise nuclear magnetic resonance apparatus. The test results reveal a hysteresis between the SFCC obtained from the freezing process and that from the thawing process. Through analyzing the test results, the hysteresis mechanism of the SFCC is attributed to supercooling. Supercooling inhibits initial pore ice formation during freezing, causing a drastic liquid water‐ice phase change once supercooling ends. Despite being considered closely related, the hysteresis of the SFCC differs from the soil water characteristic curve (SWCC), and the models used to simulate the hysteresis of SWCC cannot directly be used. To address the impact of supercooling on soil freezing‐thawing hysteresis, a novel theoretical model is proposed. Comparisons between the measured and predicted results affirm the validity of the proposed model. Plain Language Summary Understanding the freezing and thawing behavior of soils is critical for construction in cold regions. The soil freezing characteristic curve (SFCC), which describes the relationship between temperature and unfrozen water content, is essential for characterizing soil behavior during freeze‐thaw cycles. However, measuring SFCCs for both freezing and thawing presents significant challenges, often resulting in simplifications and incomplete data in many studies. In this research, we conducted freezing‐thawing tests using precise technology called nuclear magnetic resonance to examine the SFCC. We found a hysteresis between the SFCC during freezing and thawing, primarily attributed to supercooling, where the soil remains liquid below the freezing temperature. Supercooling delays initial ice formation, causing a rapid transition from liquid water to ice once it ceases. Importantly, the SFCC hysteresis differs significantly from the drying‐wetting hysteresis in the soil water characteristic curve. To address this, we propose a novel model considering the impact of supercooling on soil freezing‐thawing hysteresis. The proposed model fits well with the measured data and outperforms existing models. This study introduces a new understanding and a reliable model for soil freezing‐thawing process, contributing to better comprehension of frozen soil phase changes. Key Points Supercooling is the primary cause of freezing‐thawing hysteresis The hysteresis of the soil freezing characteristic curve differs substantively from that of the soil water characteristic curve The proposed model can address the impact of supercooling on soil freezing‐thawing hysteresis

Assessment of freezing effects on soil requires estimating the soil freezing characteristic curve (SFCC)—the variation of unfrozen water content with temperature. The existing methods for obtaining SFCCs often involve either costly experiments or heuristic inference from water retention data. Here, we propose a pore‐morphology‐based method for simple and efficient estimation of the freezing characteristic curve of water‐saturated porous media, whereby the pore‐scale configurations of water and ice phases are simulated in a digital image of porous microstructure. Idealizing the pore space as a system of overlapping spherical pores, the method simulates the freezing process with the Gibbs‐Thomson equation that can consider freezing‐point depression—a decrease in the freezing point due to spatial confinement—based on thermodynamics. For validation, we apply the proposed method to estimate the SFCC of a field soil for which the experimental freezing characteristics data are available. Results show that even with a digital pore image extracted from a surrogate discrete‐element packing of the soil, the proposed method provides an SFCC very close to the experimental data. Key Points A pore‐morphology‐based method is proposed to estimate the freezing characteristic curve of water‐saturated porous media The Gibbs‐Thomson equation and the sphere insertion method are combined to incorporate freezing point depression The proposed method is validated against experimentally measured freezing characteristic data of a field soil

The control of freezing temperatures throughout the artificial ground freezing (AGF) process is always difficult. An overly high temperature of the circulating refrigerant may lead to insufficient frozen soil strength, while an overly low temperature may cause unnecessary energy waste, and even excessive pore ice may damage the soil structure and reduce the frozen soil strength. What's more, overly freezing may damage buildings on the surface. Therefore, it is of great significance to study the optimum freezing temperature (OFT), which is very important for better and more energy-efficient employment of the AGF method. In this paper, we use uniaxial compression and direct shear tests to obtain dynamic mechanical parameters in the soil freezing process. After the analysis of varying mechanical parameters by the entropy weight TOPSIS principal component analysis method, the results show that the interval range of OFT for saturated and unsaturated sandy gravel is [− 10 °C, − 15 °C] and [− 15 °C, − 20 °C], respectively. The findings indicate that, in the AGF method, a lower temperature is not always preferable. According to the results, constructive measures to optimize the temperature field distribution in the AGF method are proposed. The research results will contribute to the assessment of the safety and efficiency of AGF projects.

Background and Aims Sulfonylureas inhibit the ATP-sensitive potassium (KATP) channel, are insulinogenic, and close the fetal ductus arteriosus. Diazoxide, a KATP channel opener, is used for neonatal hyperinsulinemic hypoglycemia, and has been associated with the reopening of the ductus arteriosus. The aim of this study is to clarify ductus-opening effect of diazoxide. Methods Neonatal rats were delivered by caesarian section near-term and incubated at 34°C. Diazoxide and pinacidil, another KATP channel opener, were injected intraperitoneally immediately, or at one hour, or at four hours postnatally, and the ductus was studied 0.5, and 1 hour later, with a rapid whole-body freezing method. Results Diazoxide and pinacidil both induced hyperglycemia. Diazoxide and pinacidil delayed neonatal ductus closure following injection immediately after birth. At 2 hours, the control ductus was closed, whereas the ductus treated with 100 mg/kg of diazoxide at birth was widely patent with a diameter 40% of the fetal ductus. Ductus diameter at 60 minutes postnatally dilated from 10% to 40% with diazoxide. Diazoxide given to the closed ductus at 4 hours after birth did not open reopen it. The ductus was more sensitive to pinacidil than to diazoxide. Conclusions Diazoxide and pinacidil open the closing ductus arteriosus of the neonatal rat. This study demonstrates that opening of KATP channels results in opening of the ductus arteriosus, indicating that the KATP channel is physiologically and pharmacologically important in ductus opening. The ductus should be checked in the neonate before and after treatment with diazoxide.

Freezing temperature parameterization significantly impacts the heat balance at sea‐ice bottom and, consequently, the simulated sea‐ice thickness. Here, the single‐column model ICEPACK was used to investigate the impact of the freezing temperature parameterization on the simulated sea‐ice thermodynamic growth during the MOSAiC expedition from October 2019 to September 2020. It is shown that large model errors exist with the standard parameterization and that different formulations for calculating the freezing temperature impact the simulated sea‐ice thickness significantly. Considering the winter mixed layer temperature, a modified parameterization of the freezing point temperature based on Mushy scheme was developed. The mean absolute error (ratio) of simulating sea‐ice thickness for all buoys reduces from 7.4 cm (4.9%) with the “Millero” scheme, which performs the best among the existing schemes in the ICEPACK model, to 4.2 cm (2.9%) with the new developed scheme. Plain Language Summary The heat transferred from the ocean to the sea‐ice influences the growth and melting of the sea‐ice. Freezing temperature is an essential parameter for calculating the heat transfer. Nevertheless, few studies have attempted to evaluate the impact of different freezing temperature parameterizations on the simulated sea‐ice thermodynamic growth. This study uses observed atmosphere and ocean data to force a single‐column model. Using different methods to calculate the freezing temperature significantly impacts the simulated sea‐ice thickness. After a series of testing and comparisons, we have developed a modified parameterization of freezing temperature that significantly reduces the simulation deviation from the observations. Key Points Different parameterizations of the freezing temperature significantly influence the simulated sea‐ice thickness A modified‐Mushy parameterization method is developed for the freezing temperature, significantly improving ice thickness simulation

The freezing medium temperature and the freezing rate are two important parameters that affect the quality of frozen product. The traditional measurement of freezing parameters will destroy the integrity of the sample and can only be implemented during the freezing process. This study aimed to develop nondestructive hyperspectral imaging (HSI) methods to rapidly detect freezing parameters. The spectral features of the porcine meat samples in frozen state were studied, in which 90 pieces of porcine samples were frozen by different methods with different freezing medium (air and liquid) at different temperatures (from −20 to −120 °C) and freezing rates (from 0.307 to 5.1 cm/h). The result showed that the freezing process would strongly influence spectra of the frozen sample. The reflectance increased with the decrease in freezing medium temperatures, and the negative correlation reached a highly significant level. The freezing parameters did not change the position of the spectral peaks but altered the spectral intensity. Most changes were near 1070, 1172, 1420, 1586, and 1890 nm. The partial least-squares regression spectral models exhibited good performance for predicting freezing medium temperatures R c 2 = 0.898 R p 2 = 0.844 and freezing rates R c 2 = 0.879 R p 2 = 0.829 . The study confirmed that could be used for measuring freezing parameters of frozen product. This novel method will not damage the sample integrity, and measurement can be implemented anytime rather than only during the freezing process by traditional methods.

The soil freezing characteristic curve (SFCC) is used as a tool for interpreting various properties of frozen soils such as hydraulic conductivity, volume change, and shear strength. Existing SFCC models are commonly based on empirical relationships, pore size distribution (POSD), or adaptations of the Soil‐Water Characteristic Curve (SWCC). Empirical models often lack a theoretical foundation, limiting their general applicability. Experimental evidence suggests that matric suction at a given temperature is affected by freezing rate, raising concerns about the suitability of SWCC‐based approaches for predicting SFCC. POSD‐based models are typically restricted to non‐saline soils, while current methods for saline soils primarily rely on SWCC‐based models, incorporating osmotic suction and iterative calculations to estimate unfrozen water content, which complicates practical applications. This study presents a unified SFCC model by transforming pore size into pore volumes using the POSD function (Weibull function) and extending the Gibbs‐Thomson equation to account for solute effects. The model eliminates the need for iterative calculations by deriving a freezing point depression equation that links the effects of confinement and solute concentration to pore size. Validations of the proposed unified SFCC model against experimental data demonstrates its accuracy in predicting unfrozen water content across different soil types and solute concentrations. Finally, the model's ability to simulate thermal‐hydraulic processes using a freezing column experiment is promising for its application in practice. Key Points A unified Soil Freezing Characteristic curve (SFCC) is proposed using the Weibull function and the extended Gibbs‐Thomson equation The residual zone of the SFCC is defined and explained using the Electrical Double Layer theory The proposed model is validated providing comparisons between measured and predicted results for various soils and solute concentrations

During near-0°C surface conditions, diverse precipitation types (p-types) are possible, including rain, drizzle, freezing rain, freezing drizzle, ice pellets, wet snow, snow, and snow pellets. Near-0°C precipitation affects wide swaths of the United States and Canada, impacting aviation, road transportation, power generation and distribution, winter recreation, ecology, and hydrology. Fundamental challenges remain in observing, diagnosing, simulating, and forecasting near-0°C p-types, particularly during transitions and within complex terrain. Motivated by these challenges, the field phase of the Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX) was conducted from 1 February to 15 March 2022 to better understand how multiscale processes influence the variability and predictability of p-type and amount under near-0°C surface conditions. WINTRE-MIX took place near the U.S.–Canadian border, in northern New York and southern Quebec, a region with plentiful near-0°C precipitation influenced by terrain. During WINTRE-MIX, existing advanced mesonets in New York and Quebec were complemented by deployment of 1) surface instruments, 2) the National Research Council Convair-580 research aircraft with W- and X-band Doppler radars and in situ cloud and aerosol instrumentation, 3) two X-band dual-polarization Doppler radars and a C-band dual-polarization Doppler radar from the University of Illinois, and 4) teams collecting manual hydrometeor observations and radiosonde measurements. Eleven intensive observing periods (IOPs) were coordinated. Analysis of these WINTRE-MIX IOPs is illuminating how synoptic dynamics, mesoscale dynamics, and microscale processes combine to determine p-type and its predictability under near-0°C conditions. WINTRE-MIX research will contribute to improving nowcasts and forecasts of near-0°C precipitation through evaluation and refinement of observational diagnostics and numerical forecast models.